def boxBetween(self, o1, o2, *args, **kargs):
batches = o1.size()[0]
num_elem = h.product(o1.size()[1:])
ei = h.getEi(batches, num_elem)
if len(o1.size()) > 2:
ei = ei.contiguous().view(num_elem, *o1.size())
return self.domain((o1 + o2) / 2, None, ei * (o2 - o1).abs() / 2).checkSizes()
def line(o1, o2, w=None):
ln = ((o2 - o1) / 2).unsqueeze(0)
if not w is None and w > 0.0:
batches = o1.size()[0]
num_elem = h.product(o1.size()[1:])
ei = h.getEi(batches, num_elem)
if len(o1.size()) > 2:
ei = ei.contiguous().view(num_elem, *o1.size())
ln = torch.cat([ln, ei * w])
return HBox((o1 + o2) / 2, None, ln).checkSizes()
def applySuper(self, ret):
batches = ret.head.size()[0]
num_elem = h.product(ret.head.size()[1:])
ei = h.getEi(batches, num_elem)
if len(ret.head.size()) > 2:
ei = ei.contiguous().view(num_elem, *ret.head.size())
ret.errors = torch.cat([ ret.errors, ei * ret.beta ]) if not ret.beta is None else ret.errors
ret.beta = None
return ret.checkSizes()
def box(original, radius):
"""
This version of it is slow, but keeps correlation down the line.
"""
batches = original.size()[0]
num_elem = h.product(original.size()[1:])
ei = h.getEi(batches, num_elem)
if len(original.size()) > 2:
ei = ei.contiguous().view(num_elem, *original.size())
return HBox(original, None, ei * radius).checkSizes()
def line(self, o1, o2, **kargs):
w = self.w.getVal(c = 0, **kargs)
ln = ((o2 - o1) / 2).unsqueeze(0)
if not w is None and w > 0.0:
batches = o1.size()[0]
num_elem = h.product(o1.size()[1:])
ei = h.getEi(batches,num_elem)
if len(o1.size()) > 2:
ei = ei.contiguous().view(num_elem, *o1.size())
ln = torch.cat([ln, ei * w])
return self.domain((o1 + o2) / 2, None, ln ).checkSizes()
def box(self, original, w, **kargs):
"""
This version of it is slow, but keeps correlation down the line.
"""
radius = self.w.getVal(c = w, **kargs)
batches = original.size()[0]
num_elem = h.product(original.size()[1:])
ei = h.getEi(batches,num_elem)
if len(original.size()) > 2:
ei = ei.contiguous().view(num_elem, *original.size())
return self.domain(original, None, ei * radius).checkSizes()
def hybrid_to_zono(self, *args, correlate=True, customRelu=None, **kargs):
beta = self.beta
errors = self.errors
if correlate and beta is not None:
batches = beta.shape[0]
num_elem = h.product(beta.shape[1:])
ei = h.getEi(batches, num_elem)
if len(beta.shape) > 2:
ei = ei.contiguous().view(num_elem, *beta.shape)
err = ei * beta
errors = torch.cat(
(err, errors), dim=0) if errors is not None else err
beta = None
return Zonotope(
self.head,
beta,
errors if errors is not None else (self.beta * 0).unsqueeze(0),
customRelu=self.customRelu if customRelu is None else None)
